






"""simple docstring"""





import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)



def   a    (     __UpperCAmelCase      :    str  )    ->							Any:
						__magic_name__:	int							=       model.config

						__magic_name__:	Optional[Any]							=       DonutSwinConfig(
						    image_size=original_config.input_size      ,   patch_size=4      ,   depths=original_config.encoder_layer      ,   num_heads=[4, 8, 1_6, 3_2]      ,   window_size=original_config.window_size      ,   embed_dim=1_2_8      ,   )
						__magic_name__:	Dict							=       MBartConfig(
						    is_decoder=__UpperCAmelCase      ,   is_encoder_decoder=__UpperCAmelCase      ,   add_cross_attention=__UpperCAmelCase      ,   decoder_layers=original_config.decoder_layer      ,   max_position_embeddings=original_config.max_position_embeddings      ,   vocab_size=len(
						        model.decoder.tokenizer  )      ,   scale_embedding=__UpperCAmelCase      ,   add_final_layer_norm=__UpperCAmelCase      ,   )

						return encoder_config, decoder_config



def   a    (     __UpperCAmelCase      :    Any  )    ->							Optional[int]:
						if "encoder.model" in name:
												__magic_name__:	List[Any]							=       name.replace("""encoder.model"""      ,   """encoder"""  )
						if "decoder.model" in name:
												__magic_name__:	List[Any]							=       name.replace("""decoder.model"""      ,   """decoder"""  )
						if "patch_embed.proj" in name:
												__magic_name__:	List[Any]							=       name.replace("""patch_embed.proj"""      ,   """embeddings.patch_embeddings.projection"""  )
						if "patch_embed.norm" in name:
												__magic_name__:	Dict							=       name.replace("""patch_embed.norm"""      ,   """embeddings.norm"""  )
						if name.startswith("""encoder"""  ):
												if "layers" in name:
																		__magic_name__:	Union[str, Any]							=       """encoder.""" + name
												if "attn.proj" in name:
																		__magic_name__:	str							=       name.replace("""attn.proj"""      ,   """attention.output.dense"""  )
												if "attn" in name and "mask" not in name:
																		__magic_name__:	Any							=       name.replace("""attn"""      ,   """attention.self"""  )
												if "norm1" in name:
																		__magic_name__:	List[Any]							=       name.replace("""norm1"""      ,   """layernorm_before"""  )
												if "norm2" in name:
																		__magic_name__:	Union[str, Any]							=       name.replace("""norm2"""      ,   """layernorm_after"""  )
												if "mlp.fc1" in name:
																		__magic_name__:	List[Any]							=       name.replace("""mlp.fc1"""      ,   """intermediate.dense"""  )
												if "mlp.fc2" in name:
																		__magic_name__:	Any							=       name.replace("""mlp.fc2"""      ,   """output.dense"""  )

												if name == "encoder.norm.weight":
																		__magic_name__:	int							=       """encoder.layernorm.weight"""
												if name == "encoder.norm.bias":
																		__magic_name__:	List[Any]							=       """encoder.layernorm.bias"""

						return name



def   a    (     __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    Union[str, Any]  )    ->							Optional[Any]:
						for key in orig_state_dict.copy().keys():
												__magic_name__:	Optional[Any]							=       orig_state_dict.pop(__UpperCAmelCase  )

												if "qkv" in key:
																		__magic_name__:	Any							=       key.split("""."""  )
																		__magic_name__:	Optional[Any]							=       int(key_split[3]  )
																		__magic_name__:	int							=       int(key_split[5]  )
																		__magic_name__:	List[Any]							=       model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																		if "weight" in key:
																								__magic_name__:	int							=       val[:dim, :]
																								__magic_name__:	int							=       val[dim : dim * 2, :]
																								__magic_name__:	int							=       val[-dim:, :]
																		else:
																								__magic_name__:	List[Any]							=       val[:dim]
																								__magic_name__:	Tuple							=       val[dim : dim * 2]
																								__magic_name__:	List[str]							=       val[-dim:]
												elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
																		# HuggingFace implementation doesn't use attn_mask buffer
																		# and model doesn't use final LayerNorms for the encoder
																		pass
												else:
																		__magic_name__:	Tuple							=       val

						return orig_state_dict



def   a    (     __UpperCAmelCase      :    Union[str, Any]      ,   __UpperCAmelCase      :    List[Any]=None      ,   __UpperCAmelCase      :    Any=False  )    ->							str:
						# load original model
						__magic_name__:	Union[str, Any]							=       DonutModel.from_pretrained(__UpperCAmelCase  ).eval()

						# load HuggingFace model
						__magic_name__,			__magic_name__:	Union[str, Any]							=       get_configs(__UpperCAmelCase  )
						__magic_name__:	List[str]							=       DonutSwinModel(__UpperCAmelCase  )
						__magic_name__:	Union[str, Any]							=       MBartForCausalLM(__UpperCAmelCase  )
						__magic_name__:	Dict							=       VisionEncoderDecoderModel(encoder=__UpperCAmelCase      ,   decoder=__UpperCAmelCase  )
						model.eval()

						__magic_name__:	Union[str, Any]							=       original_model.state_dict()
						__magic_name__:	int							=       convert_state_dict(__UpperCAmelCase      ,   __UpperCAmelCase  )
						model.load_state_dict(__UpperCAmelCase  )

						# verify results on scanned document
						__magic_name__:	Optional[Any]							=       load_dataset("""hf-internal-testing/example-documents"""  )
						__magic_name__:	Union[str, Any]							=       dataset["""test"""][0]["""image"""].convert("""RGB"""  )

						__magic_name__:	int							=       XLMRobertaTokenizerFast.from_pretrained(__UpperCAmelCase      ,   from_slow=__UpperCAmelCase  )
						__magic_name__:	str							=       DonutImageProcessor(
						    do_align_long_axis=original_model.config.align_long_axis      ,   size=original_model.config.input_size[::-1]  )
						__magic_name__:	Any							=       DonutProcessor(__UpperCAmelCase      ,   __UpperCAmelCase  )
						__magic_name__:	Union[str, Any]							=       processor(__UpperCAmelCase      ,   return_tensors="""pt"""  ).pixel_values

						if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
												__magic_name__:	Any							=       """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
												__magic_name__:	Optional[Any]							=       """When is the coffee break?"""
												__magic_name__:	Optional[Any]							=       task_prompt.replace("""{user_input}"""      ,   __UpperCAmelCase  )
						elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
												__magic_name__:	List[str]							=       """<s_rvlcdip>"""
						elif model_name in [
						    "naver-clova-ix/donut-base-finetuned-cord-v1",
						    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
						]:
												__magic_name__:	str							=       """<s_cord>"""
						elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
												__magic_name__:	List[Any]							=       """s_cord-v2>"""
						elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
												__magic_name__:	Optional[Any]							=       """<s_zhtrainticket>"""
						elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
												# use a random prompt
												__magic_name__:	List[Any]							=       """hello world"""
						else:
												raise ValueError("""Model name not supported"""  )
						__magic_name__:	Optional[int]							=       original_model.decoder.tokenizer(__UpperCAmelCase      ,   add_special_tokens=__UpperCAmelCase      ,   return_tensors="""pt"""  )[
						    """input_ids"""
						]

						__magic_name__:	Optional[int]							=       original_model.encoder.model.patch_embed(__UpperCAmelCase  )
						__magic_name__,			__magic_name__:	List[Any]							=       model.encoder.embeddings(__UpperCAmelCase  )
						assert torch.allclose(__UpperCAmelCase      ,   __UpperCAmelCase      ,   atol=1E-3  )

						# verify encoder hidden states
						__magic_name__:	Any							=       original_model.encoder(__UpperCAmelCase  )
						__magic_name__:	Optional[Any]							=       model.encoder(__UpperCAmelCase  ).last_hidden_state
						assert torch.allclose(__UpperCAmelCase      ,   __UpperCAmelCase      ,   atol=1E-2  )

						# verify decoder hidden states
						__magic_name__:	str							=       original_model(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  ).logits
						__magic_name__:	List[str]							=       model(__UpperCAmelCase      ,   decoder_input_ids=__UpperCAmelCase  ).logits
						assert torch.allclose(__UpperCAmelCase      ,   __UpperCAmelCase      ,   atol=1E-3  )
						print("""Looks ok!"""  )

						if pytorch_dump_folder_path is not None:
												print(f'Saving model and processor to {pytorch_dump_folder_path}'  )
												model.save_pretrained(__UpperCAmelCase  )
												processor.save_pretrained(__UpperCAmelCase  )

						if push_to_hub:
												model.push_to_hub("""nielsr/""" + model_name.split("""/"""  )[-1]      ,   commit_message="""Update model"""  )
												processor.push_to_hub("""nielsr/""" + model_name.split("""/"""  )[-1]      ,   commit_message="""Update model"""  )


if __name__ == "__main__":
							__lowerCamelCase							=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='naver-clova-ix/donut-base-finetuned-docvqa',
							    required=False,
							    type=str,
							    help='Name of the original model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default=None,
							    required=False,
							    type=str,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument(
							    '--push_to_hub',
							    action='store_true',
							    help='Whether or not to push the converted model and processor to the 🤗 hub.',
							)

							__lowerCamelCase							=  parser.parse_args()
							convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""




from collections.abc import Sequence



def        a__					(							snake_case__			,					snake_case__ = False							)		->   float:
			if not arr:
						return 0

			lowerCamelCase      =   0 if allow_empty_subarrays else float("""-inf"""							)
			lowerCamelCase      =   0.0
			for num in arr:
						lowerCamelCase      =   max(0 if allow_empty_subarrays else num			,					curr_sum + num							)
						lowerCamelCase      =   max(snake_case__			,					snake_case__							)

			return max_sum


if __name__ == "__main__":
					from doctest import testmod

					testmod()

					lowerCAmelCase				:     Union[str, Any]					=				[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")



